Chronic pain management and the development of opioid use disorder
Bibliographic record
Abstract
Chronic pain is a common condition that impacts quality of life and often precipitates the need for medical attention. Despite evidence that long-term opioid use provides limited relief, prescription opioid therapy remains a cornerstone in the medical management of chronic non-cancer pain. Presently, 13% of Canadians are prescribed opioids for pain management, and physicians play a crucial role in preventing the development of opioid use disorders. However, Canadian physicians lack knowledge of and comfort with evidence-based principles of opioid stewardship. In this article, we aim to highlight ongoing Canadian efforts to address physician discomfort and improve clinical practice. We focus on 2017 Canadian guidelines that provide clinicians with evidence-based recommendations for opioid use in chronic non-cancer pain management. In addition, we call attention to provincial efforts to implement physician accountability measures. In reviewing the existing literature, we uncovered inadequacies in pain management curricula within the Canadian undergraduate and continuing medical education (CME) systems. We consulted the educational practices of the European Pain Federation and the Centers for Disease Control and Prevention to make recommendations for improvement to current Canadian pain curricula. Based on our findings, we recommend that (1) Canadian medical institutions expand upon current core pain curricula, (2) pain management education be made compulsory, (3) academic detailing be emphasized as a means of CME, and (4) multidisciplinary non-medical management of chronic pain be featured more extensively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".